Prediction of Skin Diseases Using Machine Learning

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Author(s): Siddhartha Kumar Arjaria (Maulana Azad National Institute of Technology, India), Vikas Raj (Rajkiye Engineering College, Banda, India), Sunil Kumar (Rajkiye Engineering College, Banda, India), Priyanshu Shrivastava (Rajkiye Engineering College, Banda, India), Monu Kumar (Rajkiye Engineering College, Banda, India)and Jincy S. Cherian (The Bhopal School of Social Sciences, Bhopal, India)
Copyright: 2022
Pages: 25
Source title: Ethical Implications of Reshaping Healthcare With Emerging Technologies
Source Author(s)/Editor(s): Thomas Heinrich Musiolik (Berlin University of the Arts, Germany)and Alexiei Dingli (University of Malta, Malta)
DOI: 10.4018/978-1-7998-7888-9.ch008

Keywords: Ethics & Law / Medical Information Science Reference / Medical Technologies / Security & Forensics

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Abstract

Skin disease rates have been increasing over the past few decades. It has led to both fatal and non-fatal disabilities all around the world, especially in those areas where medical resources are not good enough. Early diagnosis of skin diseases increases the chances of cure significantly. Therefore, this work is comparing six machine learning algorithms, namely KNN, random forest, neural network, naïve bayes, logistic regression, and SVM, for the prediction of the skin diseases. The information gain, gain ratio, gini decrease, chi-square, and relieff are used to rank the features. This work comprises the introduction, literature review, and proposed methodology parts. In this research paper, a new method of analyzing skin disease has been proposed in which six different data mining techniques are used to develop an ensemble method that integrates all the six data mining techniques as a single one. The ensemble method used on the dermatology dataset gives improved result with 94% accuracy in comparison to other classifier algorithms and hence is more effective in this area.

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Prediction of Skin Diseases Using Machine Learning

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